Market researchers have, for years, developed strategies, techniques, and semi-automated systems for analyzing retailing and distribution information in order to identify market segments. Market segments are groups of individuals or organizations that share some type of common behavior with respect to retail purchases and actions taken after receiving distributed information, including actions taken, as one example, following accessing particular web sites and web pages. As one example, a broad market segment might be defined as males with incomes between $50,000 and $200,000 per year between the ages of 25 and 35. Individuals of this market segment may be, for example, more receptive of advertising related to motorcycles and more likely to purchase motorcycle-related items as a result of receiving such advertisements. Market segments may be defined by using values or ranges of values with many different attributes. In the case of purchasers of items from retailing web sites, these attributes may include attributes that describe the purchaser, attributes that describe the purchaser's interaction with the web site, and attributes that describe the particular information distributed to the purchasers by the web site, as one example. Because there are many different possible market segments, even when only a relatively modest number of attributes and associated attribute values are considered, it is practically impossible to propose and test market-segment definitions manually, by manual analysis, even when analysts employ computer-based statistical-analysis packages and routines. Furthermore, because of the wide variation in the frequency of occurrence of particular attribute values or ranges of attribute values in the generally very large data sets obtained, as one example, using automated web-analysis and web-optimization systems, it is difficult to employ even sophisticated automated cluster-detection methodologies in order to discover market segments. A further complexity in Internet-retailing analysis is that the information content in web sites is highly dynamic, in nature, so that an analyst cannot generally determine which particular content a particular visitor to a web site may have seen. Thus, retailers, retail-data analysts, web site developers, and many other professionals associated with retailing and marketing continue to seek effective methods and systems for market-segment discovery. Fast, reliable, and precise market-segment discovery can provide the basis for automated targeting of market segments for particular types of information distribution, promotions, and other such services to more efficiently and effectively distribute information and retail products and services.